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Python Basics for Data Science

Name: Python Basics for Data Science
Rating: 4.5 (5500 reviews)

by Joseph Santarcangelo · edX

4.5

(5,500 reviews)

300K+ enrolled5 weeksUpdated 2024-07

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Our Verdict

Worth it — with caveats

IBM's Python Basics for Data Science (course code PY0101EN, taught by IBM data scientist Joseph Santarcangelo) is a genuinely beginner-level, free-to-audit introduction to core Python plus a first taste of Pandas and NumPy, delivered as short videos and prepared Jupyter notebooks in a browser lab. Our verdict: take it (free audit only) if you are a true Python beginner who wants a low-friction on-ramp into IBM's larger Python Data Science series, but skip it if you already know Python basics or want a rigorous, problem-solving course. The realistic content load is small: it is officially a 5-week course at roughly 4-10 hours/week, yet multiple learners report finishing the material in about 5 hours. The recurring, honest criticism is that exercises are largely copy-paste in pre-built notebooks with effectively zero coding problems to solve, and the edX free track historically locks graded review questions behind the paid certificate. The certificate is not free: auditing is $0, but the verified IBM certificate costs about $99.

Solid, well-produced, and genuinely free-to-audit for absolute beginners as the first step of the IBM Python Data Science series, but the content is thin (finishable in ~5 hours), the practice is mostly copy-paste rather than real coding problems, and the free track restricts graded questions. It is worth taking only under those specific conditions.

Best for: Complete beginners with no prior Python experience (only basic math is assumed) who want a gentle, hand-held introduction using interactive Jupyter notebooks, and learners who intend to continue into IBM's broader Python for Data Science / Data Science Professional Certificate track and want the matching foundation.

Skip if: Anyone who already knows Python fundamentals (they will learn little new), self-learners who want to be challenged with real coding exercises and graded problems, and people expecting a deep data-science or machine-learning course rather than basic Python syntax with a brief Pandas/NumPy intro.

About This Course

Beginner Python course covering data types, loops, functions, and working with data using Pandas and NumPy.

What You'll Learn

Write your first Python program and work with core data types, expressions, variables, and string operations

Use Python's built-in data structures: lists, tuples, sets, and dictionaries

Apply programming fundamentals: conditions/branching, loops, functions, and basic objects and classes

Read from and write to files in Python using open()

Load, manipulate, and save tabular data with the Pandas library in a Jupyter environment

Create and operate on 1D and 2D arrays with NumPy for numerical computing

Curriculum

Module 1: Python Basics

Your first program, types, expressions and variables, and string operations.

Module 2: Python Data Structures

Lists and tuples, sets, and dictionaries.

Module 3: Python Programming Fundamentals

Conditions and branching, loops, functions, and objects and classes.

Module 4: Working with Data in Python

Reading files with open, writing files with open, and loading/saving data with Pandas.

Module 5: Working with NumPy Arrays

NumPy 1D arrays and NumPy 2D arrays (some editions also include a brief intro to APIs).

Prerequisites

Knowledge of basic mathematics (the only stated prerequisite)
No prior programming or Python experience required
A modern web browser to use the cloud-based Jupyter notebook lab (no local install needed)

Instructor

Joseph Santarcangelo

Instructor · edX

Pros & Cons

Pros

Genuinely free to audit, with a browser-based Jupyter lab so beginners can start coding immediately with zero setup
Clear, concise videos taught by an IBM data scientist (Joseph Santarcangelo) with a logical beginner-friendly progression from syntax to Pandas/NumPy
Low time commitment and effective as a quick refresher or a foundation before continuing into IBM's larger Python Data Science series
Only basic-math prerequisite, making it one of the more accessible on-ramps to data-oriented Python

Cons

Practice is largely copy-paste inside pre-built notebooks with effectively zero real coding problems to solve, so retention and skill-building are weak
Content is thin for a '5-week' course; several learners report completing it in roughly 5 hours and gaining little if they already know Python basics
On the edX free (audit) track, graded review questions have historically been locked behind the paid verified certificate
The final project can be more about environment setup than analysis, with some learners reporting 1.5-2 hours just to get the environment working

Alternatives To Consider

Intro to Machine Learning

Kaggle

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Machine Learning Crash Course

Google

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Mathematics for Machine Learning and Data Science Specialization

Coursera

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Frequently Asked Questions

Is Python Basics for Data Science free?

Yes — Python Basics for Data Science is free to access. Free to audit ($0). The verified IBM certificate costs approximately $99 (paid track). Note: the catalog currently lists certificate=false, but a paid verified certificate is in fact offered; only the free audit track lacks the certificate (and historically restricts graded review questions). The course is also available free on IBM's CognitiveClass platform.

Who is Python Basics for Data Science for?

Complete beginners with no prior Python experience (only basic math is assumed) who want a gentle, hand-held introduction using interactive Jupyter notebooks, and learners who intend to continue into IBM's broader Python for Data Science / Data Science Professional Certificate track and want the matching foundation.

What will you learn in Python Basics for Data Science?

Write your first Python program and work with core data types, expressions, variables, and string operations; Use Python's built-in data structures: lists, tuples, sets, and dictionaries; Apply programming fundamentals: conditions/branching, loops, functions, and basic objects and classes; Read from and write to files in Python using open().

What are the prerequisites for Python Basics for Data Science?

Knowledge of basic mathematics (the only stated prerequisite); No prior programming or Python experience required; A modern web browser to use the cloud-based Jupyter notebook lab (no local install needed).

Is Python Basics for Data Science worth it?

How we reviewed this course

This is an independent editorial assessment by Cursarium, based on edX's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.

Sources

Free

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